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Record W2289216137 · doi:10.1017/s071498081500063x

Connecting Socially Isolated Older Rural Adults with Older Volunteers through Expressive Arts

2016· article· fr· W2289216137 on OpenAlexaffabout
Ann MacLeod, Mark W. Skinner, Fay Wilkinson, Heather L. Reid

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2016
Typearticle
Languagefr
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsCentre for Community Based ResearchCommunity Based Research CentreRegistered Nurses' Association of OntarioTrent University
Fundersnot available
KeywordsThe artsOlder peoplePsychologyGerontologySociologyVisual artsMedicineArt

Abstract

fetched live from OpenAlex

Employing a participatory arts-based research approach, we examined an innovative program from rural Ontario, Canada, designed to address social isolation among older people. Older socially isolated adults were matched to trained volunteers, where in dyads, the eight pairs created expressive art in their home setting over the course of 10 home visits. With thematic and narrative inquiry, we analysed the experiences and perceptions of the program leader, older participants, and older volunteers via their artistic creations, weekly logs, evaluations, and field notes. The findings reveal a successful intervention that positively influenced the well-being of older adult participants and older volunteers, especially in regards to relationships, personal development, and creating meaning as well as extending the intervention's impact beyond the program's duration. We also discuss opportunities for similar programs to inform policy and enable positive community-based health and social service responses to rural social isolation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0080.005
Scholarly communication0.0020.001
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.221
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations62
Published2016
Admission routes2
Has abstractyes

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